Overview

Dataset statistics

Number of variables15
Number of observations4330
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory507.5 KiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

recency is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_days_bw_purchases is highly correlated with num_purchasesHigh correlation
num_purchases is highly correlated with recency and 3 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchases and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 1 other fieldsHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_purchases is highly correlated with revenue and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchasesHigh correlation
avg_ticket is highly correlated with avg_basket_sizeHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 1 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_returns is highly correlated with num_purchasesHigh correlation
qty_returned is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_purchases is highly correlated with revenueHigh correlation
revenue is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
customer_id is highly correlated with countryHigh correlation
country is highly correlated with customer_id and 2 other fieldsHigh correlation
recency is highly correlated with date_rangeHigh correlation
avg_days_bw_purchases is highly correlated with date_rangeHigh correlation
num_purchases is highly correlated with revenue and 1 other fieldsHigh correlation
date_range is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with country and 3 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_ticket and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_returns is highly correlated with country and 2 other fieldsHigh correlation
qty_returned is highly correlated with avg_ticket and 2 other fieldsHigh correlation
frequency is highly skewed (γ1 = 58.87555) Skewed
revenue is highly skewed (γ1 = 21.52932119) Skewed
returns_revenue is highly skewed (γ1 = -51.87290832) Skewed
avg_return_revenue is highly skewed (γ1 = -54.11806603) Skewed
qty_returned is highly skewed (γ1 = -44.92623279) Skewed
customer_id has unique values Unique
avg_days_bw_purchases has 1558 (36.0%) zeros Zeros
returns_revenue has 2825 (65.2%) zeros Zeros
avg_return_revenue has 2825 (65.2%) zeros Zeros
num_returns has 2825 (65.2%) zeros Zeros
qty_returned has 2825 (65.2%) zeros Zeros

Reproduction

Analysis started2022-02-23 18:41:16.784834
Analysis finished2022-02-23 18:42:35.296982
Duration1 minute and 18.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4330
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15300.09561
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:35.579713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12615.45
Q113812.25
median15298.5
Q316779.75
95-th percentile17984.55
Maximum18287
Range5941
Interquartile range (IQR)2967.5

Descriptive statistics

Standard deviation1721.908834
Coefficient of variation (CV)0.1125423578
Kurtosis-1.196324722
Mean15300.09561
Median Absolute Deviation (MAD)1484
Skewness0.002170505721
Sum66249414
Variance2964970.034
MonotonicityNot monotonic
2022-02-23T15:42:36.312624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
155071
 
< 0.1%
174441
 
< 0.1%
159211
 
< 0.1%
157471
 
< 0.1%
158401
 
< 0.1%
150161
 
< 0.1%
148081
 
< 0.1%
129041
 
< 0.1%
158251
 
< 0.1%
Other values (4320)4320
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%

country
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
United Kingdom
3917 
Germany
 
94
France
 
87
Spain
 
28
Belgium
 
24
Other values (30)
 
180

Length

Max length20
Median length14
Mean length13.33325635
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowUnited Kingdom
2nd rowUnited Kingdom
3rd rowFrance
4th rowUnited Kingdom
5th rowUnited Kingdom

Common Values

ValueCountFrequency (%)
United Kingdom3917
90.5%
Germany94
 
2.2%
France87
 
2.0%
Spain28
 
0.6%
Belgium24
 
0.6%
Switzerland20
 
0.5%
Portugal19
 
0.4%
Italy14
 
0.3%
Finland12
 
0.3%
Norway10
 
0.2%
Other values (25)105
 
2.4%

Length

2022-02-23T15:42:36.730112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united3919
47.4%
kingdom3917
47.4%
germany94
 
1.1%
france87
 
1.1%
spain28
 
0.3%
belgium24
 
0.3%
switzerland20
 
0.2%
portugal19
 
0.2%
italy14
 
0.2%
finland12
 
0.1%
Other values (30)128
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.16143187
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:37.112565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median51
Q3143
95-th percentile312
Maximum374
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation100.2158933
Coefficient of variation (CV)1.075722982
Kurtosis0.4189044719
Mean93.16143187
Median Absolute Deviation (MAD)40
Skewness1.244004492
Sum403389
Variance10043.22526
MonotonicityNot monotonic
2022-02-23T15:42:37.464613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2103
 
2.4%
494
 
2.2%
594
 
2.2%
389
 
2.1%
979
 
1.8%
1177
 
1.8%
1874
 
1.7%
872
 
1.7%
1070
 
1.6%
1664
 
1.5%
Other values (294)3514
81.2%
ValueCountFrequency (%)
135
 
0.8%
2103
2.4%
389
2.1%
494
2.2%
594
2.2%
648
1.1%
872
1.7%
979
1.8%
1070
1.6%
1177
1.8%
ValueCountFrequency (%)
37417
0.4%
37317
0.4%
3726
 
0.1%
3703
 
0.1%
3695
 
0.1%
3685
 
0.1%
36710
0.2%
36610
0.2%
3656
 
0.1%
3636
 
0.1%

avg_days_bw_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1155
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.4575875
Minimum0
Maximum366
Zeros1558
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:37.827449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q373.15
95-th percentile184
Maximum366
Range366
Interquartile range (IQR)73.15

Descriptive statistics

Standard deviation65.3019421
Coefficient of variation (CV)1.294194696
Kurtosis4.652342813
Mean50.4575875
Median Absolute Deviation (MAD)31
Skewness1.989512758
Sum218481.3539
Variance4264.343642
MonotonicityNot monotonic
2022-02-23T15:42:38.193143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01558
36.0%
7021
 
0.5%
4618
 
0.4%
5517
 
0.4%
4916
 
0.4%
3116
 
0.4%
9116
 
0.4%
4215
 
0.3%
2115
 
0.3%
3515
 
0.3%
Other values (1145)2623
60.6%
ValueCountFrequency (%)
01558
36.0%
19
 
0.2%
24
 
0.1%
2.8615384621
 
< 0.1%
36
 
0.1%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
44
 
0.1%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
< 0.1%
3561
 
< 0.1%
3552
< 0.1%
3521
 
< 0.1%
3512
< 0.1%
3503
0.1%

num_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.248729792
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:38.576728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.647964746
Coefficient of variation (CV)1.800059105
Kurtosis244.8058113
Mean4.248729792
Median Absolute Deviation (MAD)1
Skewness11.96826848
Sum18397
Variance58.49136475
MonotonicityNot monotonic
2022-02-23T15:42:38.927552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11506
34.8%
2827
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
Other values (46)332
 
7.7%
ValueCountFrequency (%)
11506
34.8%
2827
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
731
< 0.1%
622
< 0.1%
601
< 0.1%

date_range
Real number (ℝ≥0)

HIGH CORRELATION

Distinct374
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.926097
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:39.276938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q175
median190
Q3286
95-th percentile363
Maximum374
Range373
Interquartile range (IQR)211

Descriptive statistics

Standard deviation115.0295314
Coefficient of variation (CV)0.6153743821
Kurtosis-1.33137384
Mean186.926097
Median Absolute Deviation (MAD)106.5
Skewness0.01784712628
Sum809390
Variance13231.7931
MonotonicityNot monotonic
2022-02-23T15:42:39.633698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36630
 
0.7%
36428
 
0.6%
2526
 
0.6%
35026
 
0.6%
6526
 
0.6%
26725
 
0.6%
35725
 
0.6%
3125
 
0.6%
36524
 
0.6%
5424
 
0.6%
Other values (364)4071
94.0%
ValueCountFrequency (%)
110
0.2%
28
0.2%
310
0.2%
412
0.3%
510
0.2%
67
0.2%
77
0.2%
812
0.3%
96
0.1%
108
0.2%
ValueCountFrequency (%)
37417
0.4%
37321
0.5%
37214
0.3%
3718
 
0.2%
37011
 
0.3%
36911
 
0.3%
36815
0.3%
36720
0.5%
36630
0.7%
36524
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1403
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04742032792
Minimum0.002673796791
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:39.983140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.003283541846
Q10.01025641026
median0.01928374656
Q30.03559951209
95-th percentile0.1018023643
Maximum34
Range33.9973262
Interquartile range (IQR)0.02534310183

Descriptive statistics

Standard deviation0.5371009394
Coefficient of variation (CV)11.32638602
Kurtosis3695.324789
Mean0.04742032792
Median Absolute Deviation (MAD)0.01151511188
Skewness58.87555
Sum205.3300199
Variance0.2884774192
MonotonicityNot monotonic
2022-02-23T15:42:40.332936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0384615384627
 
0.6%
0.0185185185227
 
0.6%
0.0322580645224
 
0.6%
0.0153846153823
 
0.5%
0.0196078431423
 
0.5%
0.0526315789522
 
0.5%
0.0212765957422
 
0.5%
0.0163934426222
 
0.5%
0.0192307692321
 
0.5%
0.0312520
 
0.5%
Other values (1393)4099
94.7%
ValueCountFrequency (%)
0.00267379679116
0.4%
0.00268096514716
0.4%
0.0026881720436
 
0.1%
0.0027027027032
 
< 0.1%
0.00271002715
 
0.1%
0.0027173913045
 
0.1%
0.002724795649
0.2%
0.00273224043710
0.2%
0.0027397260276
 
0.1%
0.0027548209376
 
0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.1%
1.51
 
< 0.1%
1.3333333332
 
< 0.1%
16
0.1%
0.66666666673
0.1%
0.55227882041
 
< 0.1%
0.53494623661
 
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4234
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1915.390737
Minimum0
Maximum278778.02
Zeros10
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:40.679581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108.434
Q1298.53
median652.77
Q31609.3775
95-th percentile5648.83
Maximum278778.02
Range278778.02
Interquartile range (IQR)1310.8475

Descriptive statistics

Standard deviation8311.858379
Coefficient of variation (CV)4.339510587
Kurtosis597.0067526
Mean1915.390737
Median Absolute Deviation (MAD)454.405
Skewness21.52932119
Sum8293641.89
Variance69086989.72
MonotonicityNot monotonic
2022-02-23T15:42:41.001566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
0.2%
76.324
 
0.1%
113.53
 
0.1%
35.43
 
0.1%
153
 
0.1%
4403
 
0.1%
363.653
 
0.1%
79.23
 
0.1%
5902
 
< 0.1%
1442
 
< 0.1%
Other values (4224)4294
99.2%
ValueCountFrequency (%)
010
0.2%
1.776356839 × 10-151
 
< 0.1%
3.552713679 × 10-152
 
< 0.1%
1.065814104 × 10-141
 
< 0.1%
5.684341886 × 10-141
 
< 0.1%
2.91
 
< 0.1%
3.751
 
< 0.1%
5.91
 
< 0.1%
12.241
 
< 0.1%
12.751
 
< 0.1%
ValueCountFrequency (%)
278778.021
< 0.1%
259657.31
< 0.1%
189735.531
< 0.1%
133007.131
< 0.1%
123638.181
< 0.1%
114505.321
< 0.1%
88138.21
< 0.1%
65920.121
< 0.1%
62924.11
< 0.1%
59419.341
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4228
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369.8508748
Minimum0
Maximum13206.5
Zeros10
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:41.329028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84.73558333
Q1173.87875
median282.05875
Q3420.8925
95-th percentile890.817375
Maximum13206.5
Range13206.5
Interquartile range (IQR)247.01375

Descriptive statistics

Standard deviation464.7804866
Coefficient of variation (CV)1.256669967
Kurtosis202.4962757
Mean369.8508748
Median Absolute Deviation (MAD)118.0933333
Skewness10.63604992
Sum1601454.288
Variance216020.9007
MonotonicityNot monotonic
2022-02-23T15:42:41.732111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
0.2%
76.324
 
0.1%
79.23
 
0.1%
1203
 
0.1%
4403
 
0.1%
113.53
 
0.1%
35.43
 
0.1%
91.82
 
< 0.1%
299.752
 
< 0.1%
145.92
 
< 0.1%
Other values (4218)4295
99.2%
ValueCountFrequency (%)
010
0.2%
1.776356839 × 10-151
 
< 0.1%
3.552713679 × 10-152
 
< 0.1%
1.065814104 × 10-141
 
< 0.1%
5.684341886 × 10-141
 
< 0.1%
1.451
 
< 0.1%
3.751
 
< 0.1%
5.91
 
< 0.1%
7.51
 
< 0.1%
9.141
 
< 0.1%
ValueCountFrequency (%)
13206.51
< 0.1%
9338.381
< 0.1%
7178.6333331
< 0.1%
6207.671
< 0.1%
6181.9091
< 0.1%
4873.811
< 0.1%
4366.781
< 0.1%
4327.6216671
< 0.1%
4314.721
< 0.1%
4151.261
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2248
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.2964272
Minimum-101
Maximum12540
Zeros12
Zeros (%)0.3%
Negative2
Negative (%)< 0.1%
Memory size34.0 KiB
2022-02-23T15:42:42.111434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-101
5-th percentile29
Q179.72321429
median139.5
Q3236
95-th percentile524.1833333
Maximum12540
Range12641
Interquartile range (IQR)156.2767857

Descriptive statistics

Standard deviation328.4922066
Coefficient of variation (CV)1.623816155
Kurtosis546.3530981
Mean202.2964272
Median Absolute Deviation (MAD)71
Skewness17.76525474
Sum875943.5298
Variance107907.1298
MonotonicityNot monotonic
2022-02-23T15:42:42.543502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12019
 
0.4%
7218
 
0.4%
6417
 
0.4%
4416
 
0.4%
13616
 
0.4%
14416
 
0.4%
14615
 
0.3%
6015
 
0.3%
8815
 
0.3%
10615
 
0.3%
Other values (2238)4168
96.3%
ValueCountFrequency (%)
-1011
 
< 0.1%
-441
 
< 0.1%
012
0.3%
0.251
 
< 0.1%
0.66666666671
 
< 0.1%
12
 
< 0.1%
24
 
0.1%
34
 
0.1%
3.3333333331
 
< 0.1%
47
0.2%
ValueCountFrequency (%)
125401
< 0.1%
78241
< 0.1%
43001
< 0.1%
42801
< 0.1%
3218.4166671
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%
27081
< 0.1%
2663.9459461
< 0.1%

avg_unique_prods
Real number (ℝ≥0)

Distinct1001
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.61451109
Minimum1
Maximum297.8823529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:42.919838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.833333333
Q19.333333333
median16.85164835
Q327.75
95-th percentile56
Maximum297.8823529
Range296.8823529
Interquartile range (IQR)18.41666667

Descriptive statistics

Standard deviation19.46549533
Coefficient of variation (CV)0.9005753239
Kurtosis23.82936249
Mean21.61451109
Median Absolute Deviation (MAD)8.451648352
Skewness3.295843514
Sum93590.83303
Variance378.9055084
MonotonicityNot monotonic
2022-02-23T15:42:43.271674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101
 
2.3%
1398
 
2.3%
1088
 
2.0%
1182
 
1.9%
981
 
1.9%
1474
 
1.7%
773
 
1.7%
672
 
1.7%
872
 
1.7%
571
 
1.6%
Other values (991)3518
81.2%
ValueCountFrequency (%)
1101
2.3%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5454545451
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
1.8888888891
 
< 0.1%
ValueCountFrequency (%)
297.88235291
< 0.1%
2591
< 0.1%
2191
< 0.1%
1911
< 0.1%
1711
< 0.1%
1551
< 0.1%
1531
< 0.1%
1482
< 0.1%
1411
< 0.1%
135.33333331
< 0.1%

returns_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1064
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-108.6655543
Minimum-168469.6
Maximum0
Zeros2825
Zeros (%)65.2%
Negative1505
Negative (%)34.8%
Memory size34.0 KiB
2022-02-23T15:42:43.605199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-168469.6
5-th percentile-147.2635
Q1-14.835
median0
Q30
95-th percentile0
Maximum0
Range168469.6
Interquartile range (IQR)14.835

Descriptive statistics

Standard deviation2859.293216
Coefficient of variation (CV)-26.31278361
Kurtosis2900.915238
Mean-108.6655543
Median Absolute Deviation (MAD)0
Skewness-51.87290832
Sum-470521.85
Variance8175557.693
MonotonicityNot monotonic
2022-02-23T15:42:43.962234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02825
65.2%
-12.7520
 
0.5%
-4.9519
 
0.4%
-1517
 
0.4%
-9.9517
 
0.4%
-5.912
 
0.3%
-25.511
 
0.3%
-19.810
 
0.2%
-4.2510
 
0.2%
-3.759
 
0.2%
Other values (1054)1380
31.9%
ValueCountFrequency (%)
-168469.61
< 0.1%
-77183.61
< 0.1%
-22998.41
< 0.1%
-14688.241
< 0.1%
-8511.151
< 0.1%
-7443.591
< 0.1%
-5228.41
< 0.1%
-4815.261
< 0.1%
-4814.741
< 0.1%
-4486.241
< 0.1%
ValueCountFrequency (%)
02825
65.2%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.951
 
< 0.1%
-1.254
 
0.1%
-1.454
 
0.1%
-1.641
 
< 0.1%
-1.655
 
0.1%
-1.72
 
< 0.1%
-1.791
 
< 0.1%

avg_return_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1110
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-66.59022209
Minimum-168469.6
Maximum0
Zeros2825
Zeros (%)65.2%
Negative1505
Negative (%)34.8%
Memory size34.0 KiB
2022-02-23T15:42:44.339790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-168469.6
5-th percentile-30
Q1-6.6125
median0
Q30
95-th percentile0
Maximum0
Range168469.6
Interquartile range (IQR)6.6125

Descriptive statistics

Standard deviation2816.97859
Coefficient of variation (CV)-42.30318658
Kurtosis3081.3908
Mean-66.59022209
Median Absolute Deviation (MAD)0
Skewness-54.11806603
Sum-288335.6616
Variance7935368.375
MonotonicityNot monotonic
2022-02-23T15:42:44.724583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02825
65.2%
-12.7523
 
0.5%
-4.9521
 
0.5%
-9.9520
 
0.5%
-1517
 
0.4%
-3.7510
 
0.2%
-4.2510
 
0.2%
-179
 
0.2%
-7.59
 
0.2%
-5.99
 
0.2%
Other values (1100)1377
31.8%
ValueCountFrequency (%)
-168469.61
< 0.1%
-77183.61
< 0.1%
-4599.681
< 0.1%
-1605.0866671
< 0.1%
-1591.21
< 0.1%
-833.251
< 0.1%
-687.821
< 0.1%
-638.61913041
< 0.1%
-5941
< 0.1%
-581.41
< 0.1%
ValueCountFrequency (%)
02825
65.2%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.821
 
< 0.1%
-0.951
 
< 0.1%
-1.051
 
< 0.1%
-1.0751
 
< 0.1%
-1.1166666671
 
< 0.1%
-1.255
 
0.1%
-1.381
 
< 0.1%

num_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.949191686
Minimum0
Maximum223
Zeros2825
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-02-23T15:42:45.138131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum223
Range223
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.219247747
Coefficient of variation (CV)3.703713596
Kurtosis284.2138811
Mean1.949191686
Median Absolute Deviation (MAD)0
Skewness13.27905964
Sum8440
Variance52.11753804
MonotonicityNot monotonic
2022-02-23T15:42:45.514590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02825
65.2%
1476
 
11.0%
2284
 
6.6%
3172
 
4.0%
4117
 
2.7%
583
 
1.9%
653
 
1.2%
752
 
1.2%
841
 
0.9%
1124
 
0.6%
Other values (48)203
 
4.7%
ValueCountFrequency (%)
02825
65.2%
1476
 
11.0%
2284
 
6.6%
3172
 
4.0%
4117
 
2.7%
583
 
1.9%
653
 
1.2%
752
 
1.2%
841
 
0.9%
918
 
0.4%
ValueCountFrequency (%)
2231
< 0.1%
1331
< 0.1%
1121
< 0.1%
1111
< 0.1%
1011
< 0.1%
921
< 0.1%
901
< 0.1%
811
< 0.1%
781
< 0.1%
701
< 0.1%

qty_returned
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct216
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-61.92748268
Minimum-80995
Maximum0
Zeros2825
Zeros (%)65.2%
Negative1505
Negative (%)34.8%
Memory size34.0 KiB
2022-02-23T15:42:45.821097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-80995
5-th percentile-59.55
Q1-3
median0
Q30
95-th percentile0
Maximum0
Range80995
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1691.089558
Coefficient of variation (CV)-27.30757792
Kurtosis2066.254091
Mean-61.92748268
Median Absolute Deviation (MAD)0
Skewness-44.92623279
Sum-268146
Variance2859783.894
MonotonicityNot monotonic
2022-02-23T15:42:46.209452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02825
65.2%
-1169
 
3.9%
-2150
 
3.5%
-3105
 
2.4%
-489
 
2.1%
-678
 
1.8%
-561
 
1.4%
-1252
 
1.2%
-744
 
1.0%
-843
 
1.0%
Other values (206)714
 
16.5%
ValueCountFrequency (%)
-809951
< 0.1%
-742151
< 0.1%
-93601
< 0.1%
-90141
< 0.1%
-80041
< 0.1%
-44271
< 0.1%
-37681
< 0.1%
-33321
< 0.1%
-28781
< 0.1%
-20221
< 0.1%
ValueCountFrequency (%)
02825
65.2%
-1169
 
3.9%
-2150
 
3.5%
-3105
 
2.4%
-489
 
2.1%
-561
 
1.4%
-678
 
1.8%
-744
 
1.0%
-843
 
1.0%
-941
 
0.9%

Interactions

2022-02-23T15:42:29.614281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:34.821175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:39.914979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:44.290995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:48.279764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:52.040654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:56.405800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:00.475899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:04.392265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:08.556810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:12.628846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:17.285360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:21.376277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:25.708019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:29.936625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:35.550433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:40.205018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:44.554800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:48.551834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:52.301349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:56.671498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:00.752398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:04.716156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:08.854753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:12.884692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:17.587284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:21.673933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:25.991255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:30.286614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:35.954066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:40.513060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:44.816602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:48.816773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:52.571297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:56.961603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:01.021615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:05.009391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:09.173815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:13.224337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:17.885060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:21.943895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:26.267251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:30.569682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:36.379239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:40.803622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:45.115354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:49.095042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:52.853312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:57.256768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:01.291149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:05.303184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:09.424133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:13.529638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:18.204461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:22.245704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:26.569289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:30.842949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:36.662381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:41.107628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:45.397447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:49.342900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:53.122309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:57.541837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:01.550346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:05.568130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:09.685464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:13.789267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:18.493570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:22.524116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:26.839905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:31.121572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:36.994637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:41.372869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:45.702718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:49.625384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:53.403673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:57.825736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:01.811233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:05.907267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:09.970685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:14.159021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:18.785838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:22.861379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:27.127318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:31.404806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:37.286103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:41.739053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:46.000166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:49.879612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:53.679361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:58.111445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:02.089497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:06.222116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:10.395832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:14.504104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:19.078567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:23.223823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:27.405734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:31.699198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:37.573846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:42.102515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:46.274354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:50.130108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:53.960109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:58.379838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:02.364930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:06.559465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:10.674879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:14.803023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:19.328992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:23.494114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:27.656129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:32.013579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:37.894087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:42.395593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:46.576573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:50.395734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:54.225224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:58.772404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:02.633737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:06.829917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:10.937492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:15.542922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:19.613890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:23.798163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:27.927687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:32.324409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:38.158453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:42.722979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:46.863251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:50.675652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:54.484903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:59.042711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:02.895050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:07.092220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:11.208692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:15.819049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:19.916944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:24.087637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:28.224576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:32.605303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:38.495944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:43.028033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:47.147597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:50.924075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:55.250865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:59.332442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:03.183275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:07.367298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:11.505266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:16.078587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:20.214984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:24.430464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:28.500869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:32.917465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:38.832598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:43.336712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:47.450194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:51.210193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:55.544801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:59.618137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:03.467932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:07.635321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:11.788492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:16.365713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:20.525897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:24.752108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:28.799879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:33.250246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:39.216049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:43.624491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:47.732981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:51.487267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:55.840096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:59.922618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:03.774529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:07.928124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:12.075104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:16.653697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:20.804602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:25.043928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:29.088635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:33.529155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:39.544232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:43.996683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:47.975335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:51.760459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:41:56.102761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:00.200837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:04.069713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:08.196100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:12.345633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:16.955034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:21.076745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:25.362943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-23T15:42:29.337522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-23T15:42:46.518271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-23T15:42:47.037671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-23T15:42:47.480215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-23T15:42:47.908973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-23T15:42:34.017510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-23T15:42:34.745979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
017850United Kingdom3731.00000034134.0000005288.63155.54794148.3714298.735294-102.58-6.83866715.0-40.0
113047United Kingdom5752.83333393170.0283913089.10343.23333384.68750019.000000-143.49-6.23869623.0-35.0
212583France326.500000153710.0404316629.34441.956000292.82352915.466667-76.04-25.3466673.0-50.0
313748United Kingdom9692.66666752780.017986948.25189.65000087.8000005.6000000.000.0000000.00.0
415100United Kingdom33420.0000003400.075000635.10211.7000009.6666671.000000-240.90-80.3000003.0-22.0
515291United Kingdom2626.769231143480.0402304551.51325.107857109.1052637.285714-71.79-11.9650006.0-29.0
614688United Kingdom819.263158213660.0573775107.38243.208571119.33333315.285714-523.49-16.35906332.0-399.0
717809United Kingdom1739.666667123570.0336135344.85445.404167144.0000005.083333-67.06-33.5300002.0-41.0
815311United Kingdom14.191011913730.24396859419.34652.959780319.66101725.901099-1348.56-12.040714112.0-474.0
916098United Kingdom8847.66666772860.0244762005.63286.51857187.5714299.4285710.000.0000000.00.0

Last rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
432016000United Kingdom30.0331.00000012393.704131.2333331703.3333333.00.000.000.00.0
432115195United Kingdom30.0130.3333333861.003861.0000001404.0000001.00.000.000.00.0
432214087United Kingdom30.0130.333333181.67181.670000125.00000061.0-12.75-12.751.0-1.0
432314204United Kingdom30.0130.333333161.03161.03000082.00000036.00.000.000.00.0
432415471United Kingdom30.0130.333333469.48469.480000266.00000067.00.000.000.00.0
432513436United Kingdom20.0120.500000196.89196.89000076.00000012.00.000.000.00.0
432615520United Kingdom20.0120.500000343.50343.500000314.00000018.00.000.000.00.0
432713298United Kingdom20.0120.500000360.00360.00000096.0000002.00.000.000.00.0
432814569United Kingdom20.0120.500000227.39227.39000079.00000010.00.000.000.00.0
432912713Germany10.0111.000000794.55794.550000505.00000037.00.000.000.00.0